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Do not approve an AI tool because its vendor advertises strong security. Approve a specific use only after you have mapped the system and data, tested it in conditions that resemble deployment, examined supplier and dependency risk, assigned safeguards and owners, and documented who accepts the remaining risk. The same tool can be reasonable for low-sensitivity drafting and unacceptable for decisions involving regulated records or privileged access.
What a security assessment must decide
An AI security assessment answers a bounded question: Can this tool perform this task, for these users, with this data and these integrations, at an acceptable level of risk? It is not a generic score for the product. A hosted chatbot, an embedded feature in an existing application, an API connected to your data, and a self-hosted model create different attack surfaces and obligations.
Security cannot be separated from privacy, data handling, supplier reliability, or operational safety. A prompt containing confidential information may create a disclosure risk; a model connected to a ticketing system may create an authorization risk; a provider outage may interrupt a critical process. Assess those issues together, while obtaining legal or regulatory advice for your jurisdiction and sector where required.
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Use a written decision record. It should show the intended benefit, plausible harms, evidence collected, safeguards, residual risk, accountable approver, and conditions that force a new review. The result should be one of four explicit outcomes: proceed, proceed with restrictions, defer pending controls or evidence, or reject.
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Use NIST’s functions as a structure, not a checkbox
NIST’s AI Risk Management Framework (AI RMF) 1.0 is a voluntary, cross-sector framework organized around Govern, Map, Measure, and Manage. NIST has said the framework is being revised, so verify the current publication status before relying on it for a new program. Its Playbook provides suggested actions and references, not a universal sequence that every organization must follow.
“The NIST AI RMF Playbook is not a one-size-fits-all resource – and it is neither a checklist nor an ordered list of steps for AI actors to implement.”
— NIST AI RMF Playbook FAQs
For generative AI, NIST’s AI 600-1 profile, published July 26, 2024, calls for procurement due diligence that addresses intellectual property, privacy, security and related risks. It also supports use-case-specific supplier assessment, approved-provider inventories, monitoring and contract terms that enable evaluation of third-party processes. Treat these publications as adaptable guidance; they do not certify a tool or guarantee security.
1. Set the scope, owner and decision authority
Write the proposed use in one sentence before reviewing features. “Use an assistant” is too vague; “Customer-support supervisors use the assistant to draft replies from an approved knowledge base, with no automatic sending” is assessable. Record uses that are explicitly out of scope, such as making eligibility decisions or processing restricted records.
Define the system boundary
- Task and benefit: What business outcome is expected, and how will you know it was achieved?
- Users: Which employees, contractors, customers or automated agents can invoke the system? Are any users administrators?
- Deployment: Is it a direct vendor service, an embedded application feature, an API, an open-source model, an internally hosted model, or a combination?
- Components: Include the model, orchestration code, retrieval system, plug-ins, identity provider, storage, logging, monitoring and downstream applications.
- Third parties: List cloud hosts, model providers, data suppliers, support providers and subprocessors that can affect confidentiality, integrity or availability.
- Decision rights: Name the business owner, security reviewer, privacy or legal reviewer where applicable, technical operator and person authorized to accept residual risk.
Draw a simple data-and-control diagram. Mark trust boundaries where information, credentials or commands cross from your organization to a provider, plug-in or user-controlled environment. The diagram becomes the reference for tests, contracts and incident response.
2. Map data, permissions and information movement
Follow a representative request from entry to deletion. Do not rely on a marketing statement that data is “secure”; establish what is submitted, retrieved, retained, used for training or support, exposed in outputs and written to logs.
Capture the complete data flow
- List every input: typed prompts, uploaded files, emails, images, audio, telemetry and automatically retrieved records.
- Identify retrieval sources and their permissions. Determine whether the AI can search a shared drive, CRM, source repository, database or web service, and whether it receives the user’s permissions or a broad service account.
- Record provider processing: transient handling, storage location, retention period, backups, abuse review, support access and any use for model improvement. If a point is not established, mark it as an evidence gap rather than assuming the safest behavior.
- List outputs and destinations: screen display, generated documents, tickets, messages, API calls, databases, analytics and audit logs.
- Mark transfers across organizational or geographic boundaries and identify who can administer each boundary.
Classify information and credentials
Apply your organization’s data classification to prompts, retrieved content, outputs and logs. Identify personal information, health or financial records, authentication secrets, source code, trade secrets, export-controlled material and information subject to contractual restrictions. A low-risk prompt can become sensitive when retrieval adds confidential context.
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Check account lifecycle, multifactor authentication, single sign-on, role-based access, service-account scope, token storage, key rotation, tenant isolation and administrator actions. Ensure a user cannot obtain data merely because the model can retrieve it. Separate read, write and execute permissions, and require confirmation before an AI-generated action changes records, sends external communications or runs code.
3. Identify threats and plausible impacts
Build a threat list from the actual architecture and use case. The following categories are starting points, not a complete assessment.
- Unauthorized disclosure: prompts, retrieval results, outputs or logs reveal information to another tenant, an unapproved employee, a plug-in or the provider.
- Account compromise: stolen sessions, weak recovery, exposed API keys or excessive service-account privileges let an attacker invoke the system or reach connected data.
- Manipulated inputs: prompt injection, malicious documents, poisoned retrieval content or crafted images cause the model to ignore instructions, disclose context or take unsafe actions.
- Unsafe integrations: generated code, database queries, emails or workflow commands execute without validation, sandboxing, rate limits or human approval.
- Integrity and misuse: fabricated or altered output is treated as authoritative, or a user deliberately applies the tool outside its approved purpose.
- Provider and supply-chain failure: a subprocessor compromise, vulnerable dependency, model replacement, account suspension or service outage affects your operations.
- Availability and resilience: latency, quotas, regional outages or loss of a model version prevents a critical process from completing safely.
For each threat, describe the affected asset or person, plausible consequence, pre-existing controls, likelihood assumptions, impact severity and uncertainty. Distinguish an inconvenient bad answer from a disclosure of regulated records or an unauthorized financial transaction. Record scenarios that are not tested and explain why.
4. Perform supplier and dependency due diligence
Request evidence that relates to your use case, not a generic security page. The supplier should identify the service components and dependencies that matter to your boundary and explain how changes are governed.
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- Which model, hosting environment, regions, subprocessors and support channels handle the data?
- Are customer inputs, outputs or telemetry retained, reviewed by humans, or used to improve a model? What controls and opt-outs exist, and do they apply to every component?
- How are tenants isolated, administrators authorized and privileged actions logged?
- What vulnerability-management, penetration-testing, secure-development and incident-response evidence can be shared under appropriate confidentiality terms?
- How will you be notified of security incidents, vulnerabilities, material model changes, new subprocessors, terms changes or service retirement?
- What service levels, rate limits, backup arrangements, export facilities and recovery targets apply? Can you operate in a degraded or offline mode?
- What audit rights, independent assurance reports and cooperation obligations are available, and what limits apply?
- How can you delete data, revoke access, retrieve records and verify deletion when the contract ends?
Use a use-case-based supplier assessment. Maintain an inventory of approved providers and the purposes for which each is approved; approval for one workflow should not silently extend to another. Contractual provisions should support evaluation of third-party processes, incident cooperation, material-change notice and secure exit. If the provider will not answer a question that is material to your threat model, treat that as residual risk or a reason to defer.
5. Test in conditions that resemble deployment
Laboratory demonstrations are not deployment evidence. Construct a test plan using representative tasks, data formats, integrations, users, permissions and failure conditions. Use synthetic or minimized data when possible, and never paste live secrets into an unapproved test.
Build and document the test plan
- Define acceptance criteria. Specify confidentiality boundaries, prohibited actions, maximum tolerable error, response-time needs, logging requirements and when a human must intervene.
- Prepare scenarios. Include ordinary requests, ambiguous requests, adversarial prompts, malicious retrieved documents, oversized inputs, malformed files, revoked accounts, provider errors and conflicting instructions.
- Exercise permissions. Test each role, tenant and integration with least privilege. Verify that a user cannot retrieve records or trigger actions outside their authorization through direct prompts or indirect instructions.
- Attempt controlled attacks. Probe prompt injection, data exfiltration, cross-tenant leakage, unsafe code execution, tool misuse and denial-of-service conditions within an approved test environment.
- Verify safeguards. Confirm input filtering, output validation, sandboxing, rate limits, approval gates, redaction, alerting, rollback and shutdown mechanisms actually activate.
- Record evidence. Preserve the test version, configuration, prompts or cases, results, logs, tester, date, limitations and unresolved findings. Note which production conditions were not reproduced.
Evaluate failure behavior
Ask what happens when the model is uncertain, unavailable, manipulated or wrong. A safe design fails closed for high-impact actions, preserves an auditable record, alerts an owner and offers a tested fallback. Human review is meaningful only when the reviewer has enough context, time, authority and training to reject the output; a rubber-stamp step is not a control.
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6. Choose safeguards and assign accountable owners
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| Confidential data exposure | Block restricted classifications, redact secrets, minimize context and enforce tenant boundaries. | Data owner verifies sample flows; security verifies logs and access tests. |
| Excessive access | Use scoped service accounts, role-based retrieval, separate read/write tools and short-lived tokens. | Identity owner reviews permissions; application owner tests revocation. |
| Prompt injection and malicious content | Treat retrieved text as untrusted, isolate instructions from data, validate tool arguments and require approval for consequential actions. | Security runs adversarial cases; product owner signs off on residual paths. |
| Unreliable output | Constrain the task, cite source records where possible, apply deterministic validation and require human review for high-impact decisions. | Business owner measures error against acceptance criteria. |
| Provider outage or change | Define fallback, export and shutdown procedures; monitor notices, health and model versions. | Operations conducts recovery exercises; supplier manager tracks changes. |
| Undetected misuse | Log prompts, tool calls, approvals and administrative events in accordance with privacy rules; alert on unusual volume or destinations. | Security operations tests alert coverage; privacy owner reviews retention. |
Controls should be proportionate to impact. A drafting assistant may need strong data minimization and review, while an agent that can alter production systems requires isolation, narrowly scoped credentials, transaction limits, approval gates and a tested emergency stop.
7. Make and record the deployment decision
Compare expected benefits with negative risks and the organization’s tolerance. Do not convert uncertainty into a low score. A missing provider answer, untested integration or unknown retention rule should remain visible in the decision.
| Decision | When it is appropriate | Required record |
|---|---|---|
| Proceed | Evidence supports the intended use, safeguards work in representative tests and residual risk is accepted. | Approved scope, controls, owners, monitoring and review date. |
| Proceed with restrictions | The use is acceptable only with limits such as approved users, data classes, read-only access, human approval or a pilot. | Enforceable restrictions, exception process and exit criteria. |
| Defer | Important evidence or controls are missing but a credible remediation plan exists. | Findings, owners, deadlines and conditions for reconsideration. |
| Reject | Impact exceeds tolerance, the supplier cannot support required assurance, or the design cannot be controlled. | Reason, prohibited use and any safer alternative process. |
The approver should explicitly accept residual risk; technical reviewers should not be presumed to have that authority. Include incident escalation contacts, rollback or shutdown conditions and the date or event that triggers reassessment. NIST’s Manage guidance emphasizes weighing benefits against negative risks, while the RMF Assess process calls for reporting, remediation and updates to system plans.
8. Monitor the system throughout its lifecycle
Approval is not permanent. Monitor both security signals and behavior in production, consistent with privacy and employment requirements. Useful signals include failed authentication, unusual prompt volume, access to new data sources, blocked injection attempts, tool-call errors, policy violations, anomalous destinations, provider incidents, model-version changes, latency, outages and fallback use.
Define who reviews each signal, how quickly it is triaged and what evidence is retained. Test alerts and recovery procedures rather than assuming they work. Reassess when any of these change:
- model, prompt policy, hosting region, provider, subprocessor or dependency;
- user population, role permissions, connected application or data source;
- retention, training-use, logging or contract terms;
- business purpose, automation level or consequence of an error;
- threat intelligence, incident, vulnerability, regulatory obligation or material outage.
At retirement, revoke credentials, disable integrations, export records required for business or legal purposes, request deletion where applicable and verify that backups, logs and caches no longer provide unintended access.
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Comparing two or more AI tools fairly
Evaluate every candidate against the same task, data classification, user roles, integrations, test cases and fallback assumptions. A tool with a better general reputation is not automatically safer for your architecture.
| Comparison axis | Evidence to collect | Decision question |
|---|---|---|
| Data handling and retention | Input/output retention, training use, regions, deletion and logging behavior. | Does handling match the data classes and contractual duties? |
| Identity and integration controls | SSO, multifactor authentication, roles, service-account scope, approval gates and API limits. | Can access and actions be constrained to least privilege? |
| Model and dependency transparency | Model versions, subprocessors, plug-ins, change notices and software dependencies. | Can you understand and reassess the system boundary? |
| Supplier assurance | Incident process, vulnerability handling, independent assurance, audit cooperation and contract terms. | Will the supplier provide evidence and timely notice? |
| Testing and monitoring | Sandbox, logs, export, telemetry, alert hooks, evaluation access and version pinning. | Can your team detect, investigate and reproduce failures? |
| Resilience and fallback | Availability commitments, quotas, recovery information, offline or alternate workflow. | What happens during an outage or model withdrawal? |
| Human oversight | Review queues, explanations or source context, approval controls and override paths. | Can a qualified person stop a harmful action? |
| Residual risk | Open findings after controls, assumptions, uncertainty and acceptance authority. | Which candidate leaves the organization with tolerable, understood risk? |
A practical assessment record
Keep one version-controlled record for each use case. The following fields are sufficient to make reviews repeatable without pretending to be a regulator-approved checklist:
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- Use-case name, business owner, decision authority, users, purpose and prohibited uses.
- Architecture diagram and inventory of models, applications, data sources, integrations, providers and subprocessors.
- Data classifications, transfer locations, retention and deletion behavior, permissions and credentials.
- Threat scenarios, affected assets or people, impact reasoning, likelihood assumptions and uncertainty.
- Supplier evidence, contract terms, incident contacts, material-change process and unresolved questions.
- Test plan, representative cases, configuration, results, limitations, failed controls and remediation.
- Required safeguards, control owners, verification dates, monitoring metrics and escalation thresholds.
- Decision, scope restrictions, residual-risk approver, rollback or shutdown conditions and reassessment triggers.
These prompts derive from risk-management practice; completing them does not by itself prove compliance with a law, standard or contractual obligation.
Common assessment mistakes and corrections
Relying on a vendor badge or generic claim
Problem: A certification or security page is treated as proof that your use is safe. Correction: Map your data and permissions, request evidence relevant to the use case and test the actual integration.
Testing only normal prompts
Problem: The team measures answer quality while ignoring malicious documents, indirect instructions and revoked access. Correction: Add adversarial, malformed, unavailable and over-privileged scenarios, then verify that controls fail safely.
Giving an agent broad credentials
Problem: Convenience leads to a shared account with write access across systems. Correction: Use narrowly scoped identities, separate read and write actions, short-lived tokens, transaction limits and human approval.
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Problem: Approval silently expands from a low-risk pilot to sensitive records or autonomous actions. Correction: Put purpose, users, data classes and prohibited uses in the decision record and require reassessment for changes.
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Skipping lifecycle monitoring
Problem: A model, subprocessor, retention rule or connected data source changes after launch. Correction: Track versions and provider notices, monitor behavior and define concrete reassessment triggers.
Questions that remain organization-specific
No cross-sector framework can determine your legal basis for processing personal information, required records location, sector controls, breach-notification deadline, acceptable error rate or risk tolerance. Those answers depend on jurisdiction, industry, contracts, architecture, affected people and the consequences of failure. Involve the relevant privacy, legal, compliance, safety and engineering specialists before approving a high-impact deployment.
Frequently Asked Questions
Does using NIST AI RMF make an AI deployment compliant?
No. AI RMF 1.0 is voluntary guidance. It can organize governance and evidence, but applicable laws, contracts, sector rules and internal policies still determine your obligations.
How much testing is enough before a pilot?
There is no universal number of tests. Use representative tasks, data, permissions and integrations, include credible abuse and failure cases, document limitations, and set stricter acceptance criteria as impact and automation increase.
What if a provider will not disclose its model or subprocessors?
Assess whether the missing information is material to your threats, privacy duties or contractual requirements. Restrict the use, require compensating controls, defer approval or reject it if the uncertainty cannot be accepted.
Who should accept residual risk?
The person with documented organizational authority for the business and risk decision, supported by security, privacy, legal and technical reviewers. A tester or administrator should not be assumed to hold that authority.
When must an approved AI tool be reassessed?
Reassess after material changes to the model, provider, subprocessor, data, permissions, integrations, purpose, automation level, contract terms, threat environment or after a significant incident.
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